{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part I: Pytorch mnist demo\n",
    "In this part, we will demonstrate the usage of pytorch and sklearn.\n",
    "\n",
    "The target data is MNIST."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1. import what u need\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch     \n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from torchvision import datasets, transforms\n",
    "from torch.autograd import Variable\n",
    "import copy\n",
    "from sklearn.metrics import accuracy_score,f1_score,roc_curve,precision_recall_curve,average_precision_score,auc\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score,confusion_matrix,matthews_corrcoef,roc_auc_score\n",
    "import matplotlib.pyplot as plt\n",
    "import torch.utils.data as Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2. data preprocessing\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "BATCH_SIZE = 32\n",
    "\n",
    "#加载torchvision包内内置的MNIST数据集 这里涉及到transform:将图片转化成torchtensor\n",
    "train_dataset = datasets.MNIST(root='./data/',train=True,transform=transforms.ToTensor(),download=True)\n",
    "test_dataset = datasets.MNIST(root='./data/',train=False,transform=transforms.ToTensor())\n",
    "\n",
    "#加载小批次数据，即将MNIST数据集中的data分成每组batch_size的小块，shuffle指定是否随机读取\n",
    "train_loader = Data.DataLoader(dataset=train_dataset,batch_size=BATCH_SIZE,shuffle=True)\n",
    "test_loader = Data.DataLoader(dataset=test_dataset,batch_size=BATCH_SIZE,shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#定义网络模型亦即Net \n",
    "class Model(nn.Module):\n",
    "    # def __init__(self):\n",
    "    #     super(Model,self).__init__()\n",
    "    #     self.linear1 = nn.Linear(784,784)\n",
    "    #     self.linear2 = nn.Linear(784,10)\n",
    "    # def forward(self,X):\n",
    "    #     X = F.relu(self.linear1(X))\n",
    "    #     return F.relu(self.linear2(X))\n",
    "\n",
    "    # def __init__(self):\n",
    "    #     super(Model,self).__init__()\n",
    "    #     self.linear1 = nn.Linear(784,784)\n",
    "    #     self.linear2 = nn.Linear(784,784)\n",
    "    #     self.linear3 = nn.Linear(784,10)\n",
    "    # def forward(self,X):\n",
    "    #     X = F.relu(self.linear1(X))\n",
    "    #     X = F.relu(self.linear2(X))\n",
    "    #     return F.relu(self.linear3(X))\n",
    "\n",
    "    def __init__(self):\n",
    "        super(Model,self).__init__()\n",
    "        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)\n",
    "        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)\n",
    "        self.fc1 = nn.Linear(320, 50)\n",
    "        self.fc2 = nn.Linear(50, 10)\n",
    " \n",
    "    def forward(self, x):\n",
    "        x = F.relu(F.max_pool2d(self.conv1(x), 2))\n",
    "        x = F.relu(F.max_pool2d(self.conv2(x), 2))\n",
    "        x = x.view(-1, 320)\n",
    "        x = F.relu(self.fc1(x))\n",
    "        #x = F.dropout(x, training=self.training)\n",
    "        x = self.fc2(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Model()#.cuda() #实例化卷积层\n",
    "loss = nn.CrossEntropyLoss() #损失函数选择，交叉熵函数\n",
    "optimizer = optim.SGD(model.parameters(),lr = 0.1)\n",
    "num_epochs = 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2. Train\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "echo: 0\n",
      "lose: 0.09179882229765256\n",
      "accuracy: 0.9735833333333334\n",
      "echo: 1\n",
      "lose: 0.08037002798120181\n",
      "accuracy: 0.9753833333333334\n",
      "echo: 2\n",
      "lose: 0.07441657098531723\n",
      "accuracy: 0.9778166666666667\n"
     ]
    }
   ],
   "source": [
    "losses = [] \n",
    "acces = []\n",
    "eval_losses = []\n",
    "eval_acces = []\n",
    "\n",
    "for echo in range(num_epochs):\n",
    "    train_loss = 0   #定义训练损失\n",
    "    train_acc = 0    #定义训练准确度\n",
    "    model.train()    #将网络转化为训练模式\n",
    "    for i,(X,label) in enumerate(train_loader):     #使用枚举函数遍历train_loader\n",
    "        #X = X.view(-1,784)       #X:[64,1,28,28] -> [64,784]将X向量展平\n",
    "        X = Variable(X)#.cuda()          #包装tensor用于自动求梯度\n",
    "        label = Variable(label)#.cuda()\n",
    "        out = model(X)           #正向传播\n",
    "        lossvalue = loss(out,label)         #求损失值\n",
    "        optimizer.zero_grad()       #优化器梯度归零\n",
    "        lossvalue.backward()    #反向转播，刷新梯度值\n",
    "        optimizer.step()        #优化器运行一步，注意optimizer搜集的是model的参数\n",
    "        \n",
    "        #计算损失\n",
    "        train_loss += float(lossvalue)      \n",
    "        #计算精确度\n",
    "        _,pred = out.max(1)\n",
    "        num_correct = (pred == label).sum()\n",
    "        acc = int(num_correct) / X.shape[0]\n",
    "        train_acc += acc\n",
    "\n",
    "    losses.append(train_loss / len(train_loader))\n",
    "    acces.append(train_acc / len(train_loader))\n",
    "    print(\"echo:\"+' ' +str(echo))\n",
    "    print(\"lose:\" + ' ' + str(train_loss / len(train_loader)))\n",
    "    print(\"accuracy:\" + ' '+str(train_acc / len(train_loader)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.3. Test\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/zhangboheng/anaconda36/lib/python3.6/site-packages/torch/nn/functional.py:1386: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n",
      "  warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n"
     ]
    }
   ],
   "source": [
    "# eval_loss = 0\n",
    "# eval_acc = 0\n",
    "label_all = None\n",
    "pred_all = None\n",
    "pred_pro_all = None\n",
    "model.eval() #模型转化为评估模式\n",
    "for X,label in test_loader:\n",
    "    #X = X.view(-1,784)\n",
    "    X = Variable(X)#.cuda()\n",
    "    label = Variable(label)#.cuda()\n",
    "    testout = model(X)\n",
    "    testloss = loss(testout,label)\n",
    "    #eval_loss += float(testloss)\n",
    "\n",
    "    _, pred = testout.max(1)\n",
    "    if label_all is None:\n",
    "        label_all = label\n",
    "    else:\n",
    "        label_all = torch.cat([label_all,label])\n",
    "\n",
    "    if pred_all is None:\n",
    "        pred_all = torch.cat([pred])\n",
    "    else:\n",
    "        pred_all = torch.cat([pred_all,pred])\n",
    "\n",
    "    if pred_pro_all is None:\n",
    "        pred_pro_all = torch.cat([F.sigmoid(testout)])\n",
    "    else:\n",
    "        pred_pro_all = torch.cat([pred_pro_all,F.sigmoid(testout)])\n",
    "#     num_correct = (pred == label).sum()\n",
    "#     acc = int(num_correct) / X.shape[0]\n",
    "#     eval_acc += acc\n",
    "\n",
    "y_test = label_all.cpu().detach().numpy()\n",
    "#print(y_test)\n",
    "y_pred = pred_all.cpu().detach().numpy()\n",
    "#print(y_pred)\n",
    "y_pred_pro = pred_pro_all.cpu().detach().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ACC:0.9891000\n",
      "Precision-macro:0.9890769\n",
      "Recall-macro:0.9889935\n",
      "F1-macro:0.9890145\n"
     ]
    }
   ],
   "source": [
    "print('ACC:%.7f' %accuracy_score(y_true=y_test, y_pred=y_pred))\n",
    "print('Precision-macro:%.7f' %precision_score(y_true=y_test, y_pred=y_pred,average='macro'))\n",
    "print('Recall-macro:%.7f' %recall_score(y_true=y_test, y_pred=y_pred,average='macro'))\n",
    "print('F1-macro:%.7f' %f1_score(y_true=y_test, y_pred=y_pred,average='macro'))       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7faa90077160>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpr = dict()\n",
    "tpr = dict()\n",
    "roc_auc = dict()\n",
    "average_precision = dict()\n",
    "recall = dict()\n",
    "precision = dict()\n",
    "for i in range(10):\n",
    "    y_test2 = copy.deepcopy(y_test)\n",
    "    y_test2[y_test2!=i] = 10\n",
    "    y_test2[y_test2==i] = 1\n",
    "    y_test2[y_test2==10] = 0\n",
    "    y_pred_pro2 = y_pred_pro[:,i]\n",
    "    #print(y_pred_pro2)\n",
    "    #print(y_test2)\n",
    "    fpr[i], tpr[i], _ = roc_curve(y_test2, y_pred_pro2)\n",
    "    roc_auc[i] = roc_auc_score(y_test2,y_pred_pro2)\n",
    "\n",
    "\n",
    "    average_precision[i] = average_precision_score(y_test2, y_pred_pro2)\n",
    "    #print('Average precision-recall score: %.7f' % average_precision)\n",
    "    precision[i], recall[i], _ = precision_recall_curve(y_test2,y_pred_pro2)\n",
    "\n",
    "# Plot of a ROC curve for a specific class\n",
    "colors = ['b','g','r','k','c','m','y','#e24fff','#524C90','#845868']\n",
    "plt.figure()\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic - MNIST')\n",
    "for i in range(10):\n",
    "    plt.plot(fpr[i], tpr[i], label=\"class\" + str(i) + ':ROC curve (area = %0.3f)' % roc_auc[i],color=colors[i])\n",
    "plt.legend(loc=\"lower right\")\n",
    "#plt.savefig(\"roc.png\")\n",
    "\n",
    "\n",
    "# Plot of a ROC curve for a specific class\n",
    "colors = ['b','g','r','k','c','m','y','#e24fff','#524C90','#845868']\n",
    "plt.figure()\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.title('precision recall curve - MNIST')\n",
    "for i in range(10):\n",
    "    plt.plot(recall[i], precision[i], label=\"class\" + str(i) + ':AP (score = %0.3f)' % average_precision[i],color=colors[i])\n",
    "plt.legend(loc=\"lower right\")\n",
    "#plt.savefig(\"pro.png\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
